Deep Learning
Nicholas G. Polson, Vadim Sokolov
- 发表年份
- 2019
- 引用次数
- 4
摘要
Abstract Deep learning (DL) is a high‐dimensional data reduction technique for constructing high‐dimensional predictors in input–output models. DL is a form of machine learning that uses hierarchical layers of latent features. In this article, we review the state‐of‐the‐art of deep learning from a modelling and algorithmic perspective. We provide a list of successful areas of applications in Artificial Intelligence (AI), Image Processing, Robotics and Automation. Deep learning is predictive in its nature rather than inferential and can be viewed as a black‐box methodology for high‐dimensional function estimation.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002